An efficient graph kernel method for non-coding RNA functional prediction.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Sep 1, 2017
Abstract
MOTIVATION: The importance of RNA protein-coding gene regulation is by now well appreciated. Non-coding RNAs (ncRNAs) are known to regulate gene expression at practically every stage, ranging from chromatin packaging to mRNA translation. However the functional characterization of specific instances remains a challenging task in genome scale settings. For this reason, automatic annotation approaches are of interest. Existing computational methods are either efficient but non-accurate or they offer increased precision, but present scalability problems.